论文标题
对撞机偏见在理解种族偏见警务的统计数据中的作用
The role of collider bias in understanding statistics on racially biased policing
论文作者
论文摘要
关于使用相同数据的无武装白人，无武装的黑人是否更有可能被警察枪杀的结论矛盾。问题在于，仅依靠“警察遭遇”的数据，就有可能隐藏真正的偏见。我们提供了一个因果贝叶斯网络模型来解释这种偏见，该偏见称为对撞机偏见或伯克森的悖论，并展示了不同的结论是如何来自相同的模型和数据。我们还表明，因果贝叶斯网络为考虑偏见的替代假设和解释提供了理想的形式主义。
Contradictory conclusions have been made about whether unarmed blacks are more likely to be shot by police than unarmed whites using the same data. The problem is that, by relying only on data of 'police encounters', there is the possibility that genuine bias can be hidden. We provide a causal Bayesian network model to explain this bias, which is called collider bias or Berkson's paradox, and show how the different conclusions arise from the same model and data. We also show that causal Bayesian networks provide the ideal formalism for considering alternative hypotheses and explanations of bias.
